5711results about "Other databases clustering/classification" patented technology

Using user feedback for attack path analysis in an anomaly detection framework, including: performing an attack path analysis for a cloud deployment; receiving, from a user, user feedback for an attack vector of the attack path analysis; and initiating, based on the user feedback, a workflow for modifying one or more parameters for generating the attack path analysis.

The invention discloses a knowledge base system construction method and device for multi-source heterogeneous data and a medium, and relates to the technical field of artificial intelligence and natural language processing. The method comprises the following steps: integrating a dynamic graph convolutional network and a hierarchical attention mechanism to construct a multi-modal document analysis engine; performing semantic structure analysis on the original heterogeneous document on the basis of a multi-modal document analysis engine to extract document structure features and content semantic features, and constructing an original document relationship model on the basis of the document structure features and the content semantic features; based on the original document relationship model, performing classification fusion on heterogeneous data in the original heterogeneous document to obtain a to-be-stored heterogeneous data corpus, and processing the to-be-stored heterogeneous data corpus by using a graph neural network to establish a cross-modal semantic association index; and based on the cross-modal semantic association index, performing classified storage on the to-be-stored heterogeneous data corpora by utilizing a preset heterogeneous database so as to complete knowledge base system construction of the multi-source heterogeneous data.

Methods, apparatuses, and products for providing a code analysis feedback loop for code created using generative AI, including: performing a code analysis on code generated by a generative AI model; determining, based on the code analysis, whether the code satisfies one or more conditions; responsive to the code failing to satisfy the one or more conditions, until the code satisfies the one or more conditions, repeatedly updating the code by receiving, from the generative AI model, updated code based on a previously performed code analysis; and providing the code generated by the generative AI model.

This document relates to providing meaningful information relating to a dataset. One example can obtain aggregated summaries and a related knowledge graph. The example can enable local, community, and global retrieval augmented generation utilizing the aggregated summaries and the knowledge graph.

The invention discloses an intelligent file classification and retrieval method and system, and the method comprises the steps: carrying out the unified format conversion and feature extraction of text, image and audio data according to the original data format of a file through a multi-modal data fusion algorithm, and obtaining a standardized file feature data set; based on a deep learning adaptive classification model, performing classification processing on the standardized archive feature data set to generate multi-level archive classification labels; analyzing the semantic relationship between the archive classification labels through an enhanced semantic network construction technology, generating a semantic association map, and optimizing the semantic association map to obtain an optimized semantic association map; and according to a retrieval request input by a user, performing semantic matching and correlation sorting by utilizing the optimized semantic correlation graph, and outputting archive information most related to the retrieval request. By utilizing the embodiment of the invention, the relevance and accuracy of retrieval can be improved, and modernization and intellectualization of archive management are promoted.

Architecture for a generative artificial intelligence (AI)-enabled assistant, including: receiving, via a natural language interface for a security framework monitoring a cloud deployment, a natural language input; selecting, by a delegator generative artificial intelligence (AI) model and based on the natural language input, a particular generative AI model from a plurality of selectable generative AI models; prompting the particular generative AI model based on the natural language input; and providing, via the natural language interface and in response to the natural language input, a response from the particular generative AI model.

The invention relates to the technical field of AIGC content generation, discloses an AIGC content generation method and system based on multi-modal fusion, and aims to solve the problems of decentralization, low efficiency and insufficient originality of a traditional content generation tool. Multi-modal data such as texts, images, videos and audios are integrated, user intentions are analyzed in combination with intelligent retrieval and a domain knowledge base, automatic generation from multi-modal input to high-quality creative content is achieved, a cross-modal collaborative generation technology is adopted, semantic features are dynamically aligned, and logically coherent content is generated. The content emotional value is enhanced through an emotional analysis and dynamic optimization strategy, the homogenization bottleneck is broken through, meanwhile, an automatic quality evaluation and format adaptation mechanism is integrated, deep application of scenes such as text travel, advertisement, e-commerce and interactive network television service is supported, marketing copywriting, short videos and cross-platform distribution schemes can be efficiently generated, and the market competitiveness is improved. And the content production efficiency and the creativity transmission are obviously improved.

The invention provides an oil extraction equipment fault monitoring system and method, and belongs to the technical field of oil extraction equipment fault monitoring. The method comprises the following steps: acquiring operation data of oil extraction equipment, and performing feature extraction on the acquired operation data to obtain a target feature vector; fusing the obtained target feature vector with a historical fault case library and an oil extraction equipment physical constraint equation, and constructing a dynamically updated knowledge graph; based on the space-time causal adversarial network, analyzing the distribution offset of the target feature vector in the space-time dimension, detecting an abnormal event and outputting an abnormal type label; and according to the output abnormity type label, combining with a knowledge graph, tracing a propagation path of an abnormal event, and calculating a fault probability of a root cause component through a Bayesian network so as to carry out monitoring and early warning on the oil extraction equipment. According to the method, accurate fault detection and root cause positioning are realized through multi-modal data fusion and the dynamic causal knowledge graph, and the equipment shutdown risk and the operation and maintenance cost are remarkably reduced.

The invention provides a collocation recommendation method and system based on association rule mining, and relates to the technical field of association rule mining, and the method comprises the steps: obtaining user historical transaction data, generating a user commodity transaction score matrix, carrying out the standardization processing, and predicting the commodity score through calculating the cosine similarity between users, and obtaining an initial candidate set; carrying out normalization processing on commodity prices, calculating commodity comprehensive similarity in combination with a category hierarchical structure, carrying out clustering grouping, and screening frequent item sets meeting support degree and confidence threshold values; and finally, a commodity price change sequence is extracted to calculate a dynamic price matching degree, a multi-dimensional evaluation index is constructed, a non-dominated sorting genetic algorithm and a deep Q network are adopted for optimization, and scene-based dynamic evaluation and recommendation are realized. According to the invention, the accuracy and timeliness of commodity collocation recommendation are improved, and the interpretability of the recommendation result is enhanced.

The invention discloses an intelligent monitoring management method and system based on archive digitization, and relates to the technical field of data management, and the method comprises the steps: collecting and preprocessing multi-source archive data, employing a multi-mode BERT model to carry out the feature fusion of different data sources, and generating a unified semantic representation; semantic labeling is performed on archive data through a multi-label classification model, a semantic graph of archive content is constructed by using a graph database, an association relationship between archives is represented, a semantic index tree is constructed based on the semantic graph, and rapid positioning and calling of the archive content are optimized; and recording the change of each file version, positioning the change position based on a semantic index tree, identifying the semantic change of the file through a semantic difference comparison algorithm, recording hash, carrying out granularity division on the file content through the semantic boundary of each level of node in the index tree, and generating a user access strategy. According to the invention, dynamic perception and risk early warning of user behaviors are realized, and the intellectualization and safety of the archive management system are effectively improved.

The systems and methods disclosed herein generates responses generated by artificial intelligence (AI) models such as large language models (LLM) using intent-based rankings of retrieved information. The systems and methods disclosed herein receives an output generation request for the generation of an output using a set of AI models. Using a first AI model, a set of documents are retrieved using the received output generation request. The set of documents are partitioned into chunks. The chunks are ranked using a distance between the vector representation of the received output generation request and the vector representation of each chunk. A second AI model classifies the output generation request and chunks using an intent of the respective output generation request or chunk, and generates a second set of rankings using the intents. The set of AI models generate a response using the second set of rankings.

The invention provides a landscape garden planting planning management method and system based on terrain environment analysis, and the method comprises the steps: generating a comprehensive terrain environment database through collecting comprehensive environment and biodiversity data; generating a plant ecological adaptability prediction model by using a genetic algorithm to generate an ecological adaptability intelligent evaluation result; obtaining a first target planting combination and a layout scheme through the constructed three-dimensional virtual ecological system model in combination with a user interactive function; optimizing in combination with a style migration algorithm and an emotion perception interactive design method to obtain a final target planting combination and layout scheme; monitoring key monitoring points and indexes to obtain existing planting ecological data, and performing landscape garden planting planning management based on the target planting combination and layout scheme and the existing planting ecological data; according to the invention, intelligentization, individuation and sustainability of landscape garden planting planning are realized, and powerful support is provided for design and management of landscape gardens.

The present invention proposes a vector database retrieval method and system, which relates to the technical field of big data processing and intelligent information retrieval. The method includes preprocessing high-dimensional multimodal data to obtain a dimension reduction feature vector; constructing a cluster center based on the dimension reduction feature vector; establishing an inverted file and an intra-cluster plane index based on the cluster center, compressing the inverted file and the intra-cluster plane index to obtain a compressed inverted file and an intra-cluster plane index; obtaining a user's query vector, using the inverted file to screen candidate clusters for the query vector, introducing a hierarchical navigable small-world graph in the intra-cluster plane index, performing an intra-cluster search on the cluster cluster, and obtaining a vector database retrieval result. The present invention effectively takes into account the efficiency and accuracy of high-dimensional data retrieval, and improves the retrieval efficiency and accuracy.

The invention discloses an on-line detection method for gas in transformer oil based on photoacoustic spectrum self-correction, and relates to the technical field of spectrum detection and power equipment state monitoring, and the method comprises the following steps: S1, emitting modulation laser with a specific wavelength to a gas sample released by the transformer oil through a laser, and exciting gas molecules to generate photoacoustic signals; and S2, detecting the photoacoustic signal by using a photoacoustic sensor, converting the signal into an electric signal, and outputting the electric signal. According to the online detection method for the gas in the transformer oil, the correction coefficient is adjusted in real time through a self-correction algorithm, errors caused by light path changes and sensor aging are effectively compensated, the precision and stability of long-term detection are remarkably improved, and the detection accuracy is improved by combining a gas spectrum database and a mode recognition algorithm. According to the method, accurate qualitative and quantitative analysis of various gases is realized, the problem of cross sensitivity of multiple components is solved, and the method also has temperature and pressure compensation functions and can automatically adapt to environmental changes.

The invention relates to the technical field of data analysis, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a knowledge graph-based model analysis method, which comprises the following steps of: acquiring and processing target domain data, extracting and labeling keywords, constructing a knowledge graph comprising domain entities, a relational network and attribute characteristics, and analyzing the knowledge graph. Establishing a professional corpus and generating an analysis task, constructing a multi-dimensional analysis index system, inputting the analysis task to a to-be-analyzed model, obtaining a task output result, performing knowledge verification according to the knowledge graph, evaluating the knowledge verification result based on a multi-dimensional analysis index, and generating a final analysis result. According to the method, by fusing the knowledge graph and the multi-dimensional analysis indexes, the accuracy and comprehensiveness of evaluation are enhanced, the evaluation effect of the model in the aspects of professional knowledge understanding, reasoning ability and standard integrating degree is improved, the evaluation result is more reliable, and the application ability of the large model in the specific field is optimized.

Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

The invention discloses an algorithm for automatically extracting and identifying a photovoltaic abnormal fault, and the algorithm comprises the steps: S1, importing photovoltaic power station data, collecting and classifying power station design information data, power station historical working data and power station historical meteorological data in a database, so as to obtain first data; s2, photovoltaic power station data preprocessing is carried out, integrity and rationality checking is carried out on first data, and type conversion and normalization processing are carried out on the checked first data to obtain actual photovoltaic system operation condition data; and S3, identifying the fault of each link of the photovoltaic system. According to the algorithm for automatically extracting and identifying the photovoltaic abnormal faults, an abnormal fault sequence is rapidly and accurately extracted through data acquisition and analysis and intelligent fault prediction and diagnosis, and digital, intelligent, remote and unmanned operation and maintenance of a photovoltaic power station are achieved through accurate positioning and calculation judgment.

The invention discloses a risk management system and method based on multi-source data perception, and relates to the technical field of cloud computing security, and the method comprises the steps: collecting historical multi-source data of a cloud host; constructing a cloud host risk portrait based on historical multi-source data of the cloud host; the method comprises the following steps: dividing a cloud host network into a plurality of security domains according to service functions and security requirements, and configuring a basic access control strategy; sensing the resource use condition of the cloud host in real time, and identifying the current service state of the cloud host in combination with the service log; and matching a preset security policy according to the current service state of the cloud host and the risk portrait of the cloud host, and dynamically adjusting a network segmentation policy and an access control policy in a security domain based on a micro-isolation technology. According to the method, a micro-isolation technology is used, a network segmentation strategy and an access control strategy in a security domain are dynamically adjusted according to the real-time service state and the risk portrait of the cloud host, and the security and the stability of the cloud host in different service scenes are improved.

The invention discloses a contrast learning unsupervised cross-modal hash retrieval algorithm based on a graph attention mechanism, and relates to the technical field of cross-modal retrieval. According to the graph attention mechanism-based contrast learning unsupervised cross-modal Hash retrieval algorithm, feature extraction is performed on image and text data through a pre-trained CLIP model to obtain image modal features and text modal features, and additional positive samples are generated through data enhancement; the similarity is calculated through Euclidean distance and cosine similarity on the basis of a graph attention network, and high weight is given to positive samples and low weight is given to negative samples by comparing adjacent matrixes. The method has the advantages that the features of the image mode and the text mode can be effectively fused and mapped to the common Hash space, a graph attention mechanism can adaptively adjust the connection weight between the features of the different modes, samples with high semantic correlation are highlighted, and therefore the semantic consistency between the image and the text is ensured.

The invention provides a retrieval enhancement generation method and system based on a large language model. The method comprises the steps that input query information of a user is obtained; performing rewriting and key information extraction on the input query information based on a large language model to obtain a first query instruction and a second query instruction; according to the input query information, identifying a user intention based on a large language model and performing intention classification to obtain an intention classification result; according to the first query instruction and the second query instruction, performing multi-path mixed retrieval through a preset multi-source knowledge base to obtain an initial retrieval result; according to the intention classification result, performing result rearrangement and screening on the initial retrieval result to obtain an enhanced cue word; and inputting the input query information and the enhanced cue word into the large language model to obtain a retrieval enhanced generation result. According to the method, the query demand and intention of the user can be accurately recognized, related information is retrieved in an open domain in a high-precision manner, and the instantaneity, the accuracy, the specialty and the reply quality of information retrieval are improved.

The invention discloses a hyper-converged server multi-resource integration system and a scheduling method, belongs to the technical field of computer resource management, and aims at solving the problems that a traditional hyper-converged server is dispersed in resource scheduling, low in storage efficiency, difficult to identify abnormities and the like. Multi-source data is obtained by means of distributed acquisition nodes, a distributed time sequence database architecture is adopted for storage, a standard data set containing a multi-dimensional index is constructed, and efficient storage and rapid retrieval of the data are achieved. Positioning target data to construct a graph model, building a layered and partitioned distributed graph database, and capturing change events in real time to realize dynamic updating. And mining a resource causal relationship by using a graph neural network, and screening an effective causal chain. Meanwhile, according to a graph model, monitoring weights of nodes and connecting edges are calculated, differential monitoring is implemented, potential abnormal points are accurately identified, and a resource scheduling strategy is generated in combination with a causal relationship. Real-time feedback adjustment and database updating are performed during execution, multi-resource deep integration and intelligent scheduling are achieved, and the resource utilization rate and stability of the system are remarkably improved.

The present invention relates to a social recommendation method based on cross-topic contrastive learning, comprising the following steps: acquiring a user-article interaction graph in a recommendation system; carrying out article representation learning, and carrying out article clustering and topic division; constructing a topic-based social relationship by means of cross-topic contrast; on the basis of a balance theory, propagating information within a topic on the basis of an SGCN; on the basis of an extended structural balance theory, constructing objective functions of a self-supervised task under different topics; in a recommendation task, using a Bayesian personalized ranking loss and combining objective functions of cross-topic contrastive learning with the objective functions of the self-supervised task based on the balance theory to determine a total loss function of the recommendation system; and training the recommendation system on the basis of the total loss function to obtain a recommendation result considering user differences. Compared with the prior art, in the present invention, high-order social relationships can be constructed under different topics, so that interest preferences of users under different topics are modeled, thereby mitigating the problem of construction conflicts of signed network-based social relationships.

The invention relates to the technical field of medical document management, in particular to a digital management method and system for medical documents. Obtaining medical document images of a target medical business system, processing the medical document images, and generating a standardized text stream of unified coding of each medical document image; constructing a feature matrix with spatial-semantic dual attributes in a feature region in each medical document through the standardized text stream of the unified coding of each medical document image; and extracting an entity relationship triple in the feature matrix, obtaining semantic fingerprint features of each medical document according to the entity relationship triple, and classifying each medical document according to the semantic fingerprint features of each medical document. According to the invention, efficient and accurate classification of the medical documents can be realized, and the intelligent and automatic level of medical document management is improved.

The invention belongs to the technical field of e-commerce, and particularly relates to a big data-based recommendation algorithm design in an e-commerce platform, which comprises the following steps: completing data preprocessing based on data collection and preprocessing; constructing user portraits to generate personalized user portraits, and classifying the users by using a clustering algorithm to find group features of the users; analyzing the user similarity and the commodity similarity, and calculating the similarity between the users; carrying out user-based collaborative filtering and article-based collaborative filtering, and carrying out dimensionality reduction processing on user and commodity features by using an SVD or latent semantic model; modeling user behaviors by using a neural network, and performing data compression and feature extraction by using an auto-encoder; the user feedback data is collected for continuous optimization and iteration of the model, and online learning and A / B test are adopted to adjust the recommendation strategy, so that the method has the effects of realizing more accurate and personalized commodity recommendation, and improving the shopping experience of the user and the sales conversion rate of the platform.

The invention relates to the technical field of computers, in particular to a multi-modal data search method, device and equipment based on a generative AI large model, a storage medium and a product. The method comprises the following steps: acquiring various types of data from a plurality of heterogeneous data sources to obtain acquired data; performing feature information extraction on the acquired data based on a preset generative AI large model to obtain feature data; constructing a multi-modal index structure based on the feature data; matching the search instruction with index data in the multi-modal index structure; and fusing and sorting the matching results to obtain a target search result. According to the method, various types of data are collected from a plurality of heterogeneous data sources, a multi-modal index structure is constructed after feature information extraction is performed on the data by adopting a generative AI large model, and the search instruction is efficiently matched with the various types of data, so that the efficiency and accuracy of data query are improved.

The invention discloses a hierarchical use method based on sensitive data, and belongs to the technical field of data information security, and the method comprises the steps: basic data collection: obtaining overall data information which needs to be used by a demand side to the inside or the outside; identifying and preprocessing data; sensitive data storage processing: for sensitive data, storing the sensitive data in a private cloud or configuring a secret-related server; a data sensitivity evaluation model, which is used for formulating a sensitive data evaluation model according to the use route of the sensitive data through different policies; sensitive data classification processing: performing label processing on the data according to a result output by model evaluation, and providing corresponding limitation when the data is provided outwards; grading use of sensitive data; sensitive data use logs; a centralized management and control platform is used for data. According to the invention, through intelligent classification, authority control and dynamic adjustment of the access strategy, the security and compliance of the sensitive information in different use scenes are ensured.

The invention relates to the technical field of database optimization, in particular to a slow SQL (Structured Query Language) statement optimization method and system, electronic equipment and a storage medium, the slow SQL statement optimization method comprises the following steps: preprocessing a collected target slow SQL statement to obtain multi-modal data of the target slow SQL statement; inputting the multi-modal data of the target slow SQL statement into the multi-modal large model, and performing feature extraction and fusion to generate comprehensive semantic representation; generating an optimization suggestion of the target slow SQL statement according to the comprehensive semantic representation; the optimization suggestions are verified, and then the optimization suggestions which are verified to be qualified are executed in a database. Through multi-dimensional data acquisition, large-model deep analysis and automatic strategy execution, full-life-cycle quality detection and optimization of the slow SQL statement are realized.

The invention discloses a knowledge base index construction optimization method and device based on a large model, and the method comprises the steps: dividing a text document into a plurality of configuration blocks, carrying out the vectorization embedding of text blocks to obtain a vector database, and carrying out the extraction of graph elements and the combination of entities to obtain a combination result. Performing entity analysis on a merging result to obtain a structured entity and a relational data graph, grouping based on a relationship closeness degree between the structured entity and the relational data graph to obtain a community structure with similar characteristics, generating a community abstract report by utilizing a large model, integrating the community abstract report into a graph database, and finally, constructing a graph database based on a naive RAG method and a knowledge graph technology. And combining the vector database and the graph database to obtain a knowledge base multi-dimensional index. According to the method, the knowledge graph is adopted to construct a knowledge base index through complex connections and invisible relations in knowledge, meanwhile, a large model technology is adopted to intelligently assist the knowledge graph generation process, a knowledge community is constructed to supplement dominant and invisible relations between the knowledge, and the efficiency and accuracy of knowledge retrieval and generation are improved.

A method, computer program product, and computing system for processing training data and prediction data as a plurality of tokens using a classification-based machine learning model. A plurality of weighting features associated with the training data and the prediction data are defined by processing the output of the machine learning model with an attention layer. The plurality of weighting features are reshaped to generate weights for a trained neural network by processing the plurality of weighting features with an attention layer.

The invention relates to the technical field of electric digital data processing, in particular to a traditional Chinese medicine data management system and method for data retrieval. The method comprises the following steps: acquiring morphology data of a traditional Chinese medicinal material sample; performing grade evaluation based on the traditional Chinese medicinal material morphology data to generate traditional Chinese medicinal material grade label data; acquiring a to-be-detected traditional Chinese medicinal material image; preprocessing the to-be-detected traditional Chinese medicinal material image, and performing entity recognition and relation extraction to obtain traditional Chinese medicinal material semantic annotation data; and fusing the traditional Chinese medicinal material grade label data and the traditional Chinese medicinal material semantic annotation data to obtain traditional Chinese medicinal material semantic annotation enhanced data. According to the method, the information of the traditional Chinese medicinal materials is expressed through structured processing and semantic enhancement, the knowledge graph containing dominant and implicit semantic relationships is constructed, and the retrieval depth, flexibility and intelligent application of knowledge of the traditional Chinese medicinal materials are improved.